Comparing the performance of screening surveys versus predictive models in identifying patients in need of health-related social need services in the emergency department.

ABSTRACT:

Background

Health-related social needs (HRSNs), such as housing instability, food insecurity, and financial strain, are increasingly prevalent among patients. Healthcare organizations must first correctly identify patients with HRSNs to refer them to appropriate services or offer resources to address their HRSNs. Yet, current identification methods are suboptimal, inconsistently applied, and cost prohibitive. Machine learning (ML) predictive modeling applied to existing data sources may be a solution to systematically and effectively identify patients with HRSNs. The performance of ML predictive models using data from electronic health records (EHRs) and other sources has not been compared to other methods of identifying patients needing HRSN services.

Methods

A screening qu

SUBMITTER: Mazurenko O 

PROVIDER: S-EPMC11578524 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature